Abstract
Renewable energy sources such as solar and wind are now competitive with traditional fossil and nuclear power when generating but that is just the challenge. When not generating can be a problem for grid integration and the main challenge to the widespread acceptance and dissemination of solar and wind, and the focus of research for the next generation of energy engineers. Intermittent, the adjective most associated with solar and wind energy has been and continues to be the focus of research by power engineers, AI professionals, and system scientists from the late 20th century and is the critical factor in the design of the future power grids, The most obvious solution is energy storage but then the choice of the storage method and size are complex problems. Will best solutions involve pumped hydro, Li-Ion batteries, or hydrogen generation? Or will next-generation ultra-capacitors, or high-speed flywheels gyros, or some yet to be discovered device will be the dominating technologies? The primary objective of the storage designs will be based on what's best for the reliability and efficiency of the grid, and simultaneously optimizing cost and environmental impact functions. Socio-economic and geopolitical considerations must also be considered to satisfy local or regional constraints. There is also the question of purpose: will it be sized for grid stability, or medium, or long-term storage. This factor will depend on the specific grid requirements. The focus of this paper is to study multi-source renewable energy systems that include storage called HRES or Hybrid Renewable Energy with Storage. This study describes the development of a behind-the-meter Energy Management System (EMS) for an HRES, under the assumption that Real-Time Pricing (RTP) is offered by a utility supplying power to a medium-size office complex. A cost function to be minimized is introduced to optimize the contribution of each energy source. Also, this work develops the basis of a platform for decision-makers to evaluate the viability of the optimized system in conjunction with the financial feasibility analysis.

Abstract
Health Remote Monitoring Systems (HRMS) offer the ability to address health-care human resource concerns. In developing nations, where pervasive mobile networks and device access are linking people like never before, HRMS are of special relevance. A fundamental aim of this research work is the realization of technological-based solution to triage and follow-up people living with dementias so as to reduce pressure on busy staff while doing this from home so as to avoid all unnecessary visits to hospital facilities, increasingly perceived as dangerous due to COVID-19 but also raising nosocomial infections, raising alerts for abnormal values. Sensing approaches are complemented by advanced predictive models based on Machine Learning (ML) and Artificial Intelligence (AI), thus being able to explore novel ways of demonstrating patient-centered predictive measures. Low-cost IoT devices composing a network of sensors and actuators aggregated to create a digital experience that will be used and exposure to people to simultaneously conduct several tests and obtain health data that can allow screening of early onset dementia and to aid in the follow-up of selected cases. The best ML for predicting AD was logistic regression with an accuracy of 86.9%. This application as demonstrated to be essential for caregivers once they can monitor multiple patients in real-time and actuate when abnormal values occur.

Abstract
In this paper an Artificial Bee Colony Approach for Scheduling Optimization is presented. The adequacy of the proposed approach is validated on the minimization of the total weighted tardiness for a set of jobs to be processed on a single machine and on a set of instances for Job-Shop scheduling problem. The obtained computational results allowed concluding about their efficiency and effectiveness. The ABC performance and respective statistical significance was evaluated.

Abstract
In the current global market organizations face uncertainties and shorter response time. In order to remain competitive many organizations adopted flexible resources capable of performing several operations with different performance capabilities. The unrelated parallel-machines makespan minimization problem (RmIiCmax) is known to be NP-hard or too complex to be solved exactly. Among the several heuristics used for solving this problem, it is possible to identify MCT (Minimum Completion Time) that allocates tasks in a random order to the minimum completion time machine. This paper proposes an ordered approach to the MCT heuristic. MOMCT (Modified Ordered Minimum Completion Time), which will order tasks in accordance to the mean difference of the completion time on each machine and the minimum completion time machine. The computational study demonstrated the improved performance of the proposed ordered approach to the MCT heuristic.

Abstract
Scheduling problems occurring in parallel machines manufacturing environments are quite usual and many different methods have been applied for solving it. These methods vary from the application of more or less simple heuristics and rules up to more complex methods, including distinct kind of metaheuristics. In this paper we discuss a fuzzy optimization method using simulated annealing (Fuzzy-SA) for solving an unrelated parallel machines manufacturing scheduling problem. To demonstrate the potential of our method we use an illustrative example of a parallel machines scheduling (PMS) problem and then we analyse it and perform statistical tests with 20 instances.

Abstract
Extended Manufacturing Environments (EMEs) are nowadays growing due to the increase on Distributed and Virtual Enterprises, which led to an emergent need to apply scheduling approaches accordingly. This can be achieved in several different ways, namely by putting forward new approaches or by trying to adapt existing ones. In this paper the adaptation of some existing scheduling methods is proposed for solving a two stage manufacturing scheduling problem, and an illustrative example is presented. Several approaches were analyses, namely through the use of the ANOV A and the Post Hoc Scheffe's test, that demonstrated the superior performance of one of the proposed methods.